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example_mamba_chunk_state.py
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# Copyright (c) Tile-AI Corporation.
# Licensed under the MIT License.
import argparse
import torch
import torch.nn.functional as F
import tilelang
from tilelang.autotuner import *
import tilelang.language as T
from einops import rearrange, repeat
import itertools
def chunk_state_triton(B, x, dt, dA_cumsum):
from mamba_ssm.ops.triton.ssd_chunk_state import _chunk_state_fwd
return _chunk_state_fwd(B, x, dt, dA_cumsum, states_in_fp32=False)
def ref_program(B, x, dt, dA_cumsum):
"""
Argument:
B: (batch, seqlen, ngroups, headdim)
x: (batch, seqlen, nheads, headdim)
dt: (batch, nheads, nchunks, chunk_size)
dA_cumsum: (batch, nheads, nchunks, chunk_size)
Return:
states: (batch, nchunks, nheads, headdim, dstate)
"""
# Check constraints.
batch, seqlen, nheads, headdim = x.shape
dstate = B.shape[-1]
_, _, nchunks, chunk_size = dt.shape
assert seqlen <= nchunks * chunk_size
assert x.shape == (batch, seqlen, nheads, headdim)
assert dt.shape == (batch, nheads, nchunks, chunk_size)
ngroups = B.shape[2]
assert nheads % ngroups == 0
assert B.shape == (batch, seqlen, ngroups, dstate)
B = repeat(B, "b l g d -> b l (g h) d", h=nheads // ngroups)
assert dA_cumsum.shape == (batch, nheads, nchunks, chunk_size)
if seqlen < nchunks * chunk_size:
x = F.pad(x, (0, 0, 0, 0, 0, nchunks * chunk_size - seqlen))
B = F.pad(B, (0, 0, 0, 0, 0, nchunks * chunk_size - seqlen))
x = rearrange(x, "b (c l) h p -> b c l h p", l=chunk_size)
B = rearrange(B, "b (c l) ... -> b c l ...", l=chunk_size)
decay_states = torch.exp((dA_cumsum[:, :, :, -1:] - dA_cumsum))
return torch.einsum("bclhn,bhcl,bhcl,bclhp->bchpn", B.to(x.dtype), decay_states.to(x.dtype),
dt.to(x.dtype), x)
def get_configs():
block_M = [64, 128]
block_N = [32, 64, 128]
block_K = [32, 64]
num_stages = [1, 2, 3, 4, 5]
_configs = list(itertools.product(block_M, block_N, block_K, num_stages))
configs = [{
'block_M': c[0],
'block_N': c[1],
'block_K': c[2],
'num_stages': c[3],
'threads': c[0] * 2
} for c in _configs]
return configs
def chunk_state_fwd(batch, seqlen, chunk_size, ngroups, nheads, headdim, dstate, tune=False):
dtype = "float16"
accum_dtype = "float"
nchunks = T.ceildiv(seqlen, chunk_size)
p = 1.44269504
def kernel_func(block_M, block_N, block_K, num_stages, threads):
@T.prim_func
def main(B: T.Tensor((batch, seqlen, ngroups, dstate), dtype), x: T.Tensor(
(batch, seqlen, nheads, headdim), dtype), dt: T.Tensor(
(batch, nheads, nchunks, chunk_size), dtype), dA_cumsum: T.Tensor(
(batch, nheads, nchunks, chunk_size), dtype), Output: T.Tensor(
(batch, nchunks, nheads, headdim, dstate), dtype)):
with T.Kernel(
nheads,
T.ceildiv(headdim, block_M) * T.ceildiv(dstate, block_N),
batch * nchunks,
threads=threads) as (bz, bx, by):
x_shared = T.alloc_shared((block_K, block_M), dtype)
x_local = T.alloc_fragment((block_K, block_M), dtype)
xt_local = T.alloc_fragment((block_M, block_K), dtype)
B_shared = T.alloc_shared((block_K, block_N), dtype)
dt_shared = T.alloc_shared((block_K), dtype)
dA_cumsum_shared = T.alloc_shared((block_K), dtype)
acc_o = T.alloc_fragment((block_M, block_N), accum_dtype)
acc_o_shared = T.alloc_shared((block_M, block_N), dtype)
scale = T.alloc_fragment((block_K), accum_dtype)
dA_cs_last = T.alloc_fragment((1), accum_dtype)
dA_cumsum_local = T.alloc_fragment((block_K), accum_dtype)
dt_local = T.alloc_fragment((block_K), accum_dtype)
loop_range = T.ceildiv(chunk_size, block_K)
batch_idx = by % batch
chunk_idx = by // batch
m_idx = bx // T.ceildiv(dstate, block_N)
n_idx = bx % T.ceildiv(dstate, block_N)
T.annotate_layout({
x_shared: tilelang.layout.make_swizzled_layout(x_shared),
acc_o_shared: tilelang.layout.make_swizzled_layout(acc_o_shared)
})
dA_cs_last[0] = dA_cumsum[batch_idx, bz, chunk_idx, chunk_size - 1]
T.clear(acc_o)
for k in T.Pipelined(loop_range, num_stages=num_stages):
T.copy(
x[batch_idx, chunk_idx * chunk_size + k * block_K:chunk_idx * chunk_size +
(k + 1) * block_K, bz, m_idx * block_M:(m_idx + 1) * block_M], x_shared)
T.copy(dA_cumsum[batch_idx, bz, chunk_idx, k * block_K:(k + 1) * block_K],
dA_cumsum_shared)
T.copy(dt[batch_idx, bz, chunk_idx, k * block_K:(k + 1) * block_K], dt_shared)
T.copy(dA_cumsum_shared, dA_cumsum_local)
T.copy(dt_shared, dt_local)
for i in T.Parallel(block_K):
scale[i] = T.exp2(dA_cs_last[0] * p - dA_cumsum_local[i] * p) * dt_local[i]
T.copy(x_shared, x_local)
for i, j in T.Parallel(block_M, block_K):
xt_local[i, j] = x_local[j, i] * scale[j]
T.copy(
B[batch_idx, chunk_idx * chunk_size + k * block_K:chunk_idx * chunk_size +
(k + 1) * block_K, bz // (nheads // ngroups),
n_idx * block_N:(n_idx + 1) * block_N], B_shared)
T.gemm(xt_local, B_shared, acc_o)
T.copy(acc_o, acc_o_shared)
T.copy(
acc_o_shared,
Output[batch_idx, chunk_idx, bz, m_idx * block_M:(m_idx + 1) * block_M,
n_idx * block_N:(n_idx + 1) * block_N])
return main
if tune:
@autotune(configs=get_configs(), warmup=10, rep=10)
@jit(out_idx=[4], supply_type=tilelang.TensorSupplyType.Normal, ref_prog=None)
def kernel(block_M=None, block_N=None, block_K=None, num_stages=None, threads=None):
return kernel_func(block_M, block_N, block_K, num_stages, threads)
return kernel()
else:
def kernel(block_M, block_N, block_K, num_stages, threads):
return kernel_func(block_M, block_N, block_K, num_stages, threads)
return kernel
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--batch', type=int, default=8, help='batch size')
parser.add_argument('--heads', type=int, default=80, help='heads')
parser.add_argument('--groups', type=int, default=1, help='groups')
parser.add_argument('--seq_len', type=int, default=4096, help='sequence length')
parser.add_argument('--chunk_size', type=int, default=256, help='chunk size')
parser.add_argument('--dim', type=int, default=64, help='dim')
parser.add_argument('--dstate', type=int, default=128, help='dstate')
parser.add_argument('--tune', action='store_true', help='tune configs')
args = parser.parse_args()
batch, heads, groups, seq_len, chunk_size, dim, dstate = args.batch, args.heads, args.groups, args.seq_len, args.chunk_size, args.dim, args.dstate
total_flops = 2 * batch * seq_len * heads * dim * dstate
if (not args.tune):
program = chunk_state_fwd(
batch, seq_len, chunk_size, groups, heads, dim, dstate, tune=args.tune)(
block_M=64, block_N=128, block_K=64, num_stages=4, threads=128)
kernel = tilelang.compile(program, out_idx=[4])
profiler = kernel.get_profiler(tilelang.TensorSupplyType.Normal)
profiler.assert_allclose(ref_program, rtol=0.01, atol=0.01)
print("All checks pass.")
latency = profiler.do_bench(ref_program, warmup=500)
print("Ref: {:.2f} ms".format(latency))
print("Ref: {:.2f} TFlops".format(total_flops / latency * 1e-9))
latency = profiler.do_bench(warmup=500)
print("Tile-lang: {:.2f} ms".format(latency))
print("Tile-lang: {:.2f} TFlops".format(total_flops / latency * 1e-9))
else:
best_result = chunk_state_fwd(
batch, seq_len, chunk_size, groups, heads, dim, dstate, tune=args.tune)
best_latency = best_result.latency
best_config = best_result.config
ref_latency = best_result.ref_latency
print(f"Best latency: {best_latency}")
print(f"Best TFlops: {total_flops / best_latency * 1e-9}")
print(f"Best config: {best_config}")